// SPDX-License-Identifier: LGPL-3.0-or-later
#include <gtest/gtest.h>

#include <iostream>

#include "device.h"
#include "env_mat.h"
#include "fmt_nlist.h"
#include "neighbor_list.h"
#include "prod_force.h"

template <typename T>
inline void double_vec(std::vector<T>& v) {
  v.insert(std::end(v), std::begin(v), std::end(v));
}

class TestProdForceR : public ::testing::Test {
 protected:
  std::vector<double> posi = {12.83, 2.56, 2.18, 12.09, 2.87, 2.74,
                              00.25, 3.32, 1.68, 3.36,  3.00, 1.81,
                              3.51,  2.51, 2.60, 4.27,  3.22, 1.56};
  std::vector<int> atype = {0, 1, 1, 0, 1, 1};
  std::vector<double> posi_cpy;
  std::vector<int> atype_cpy;
  int nframes = 2;
  int ntypes = 2;
  int nloc, nall, nnei, ndescrpt;
  double rc = 6;
  double rc_smth = 0.8;
  SimulationRegion<double> region;
  std::vector<int> mapping, ncell, ngcell;
  std::vector<int> sec_a = {0, 5, 10};
  std::vector<int> sec_r = {0, 0, 0};
  std::vector<int> nat_stt, ext_stt, ext_end;
  std::vector<std::vector<int>> nlist_a_cpy, nlist_r_cpy;
  std::vector<double> net_deriv, in_deriv;
  std::vector<double> env, env_deriv, rij_a;
  std::vector<int> nlist;
  std::vector<int> fmt_nlist_a;
  std::vector<double> expected_force = {
      8.47974,  -14.40744, -6.87937,  -20.21879, 5.47417,   14.86084, -0.70576,
      9.63198,  -8.41144,  -17.41399, 6.52118,   -13.13187, 2.88846,  -16.66137,
      25.88393, 26.73044,  9.31580,   -12.21548, -7.12901,  -7.68483, 4.98461,
      -4.37014, -1.12277,  2.89025,   0.00000,   0.00000,   0.00000,  0.00000,
      0.00000,  0.00000,   0.00000,   0.00000,   0.00000,   0.00000,  0.00000,
      0.00000,  0.00000,   0.00000,   0.00000,   0.00000,   0.00000,  0.00000,
      0.00000,  0.00000,   0.00000,   0.00000,   0.00000,   0.00000,  0.00000,
      0.00000,  0.00000,   0.00000,   0.00000,   0.00000,   0.00000,  0.00000,
      0.00000,  0.00000,   0.00000,   0.00000,   0.00000,   0.00000,  0.00000,
      0.00000,  0.00000,   0.00000,   0.00000,   0.00000,   0.00000,  0.00000,
      0.00000,  0.00000,   0.00000,   0.00000,   0.00000,   0.00000,  0.00000,
      0.00000,  0.00000,   0.00000,   0.00000,   0.00000,   0.00000,  0.00000,
      0.00000,  0.00000,   0.00000,   0.00000,   0.00000,   0.00000,  0.00000,
      0.00000,  0.00000,   0.00000,   0.00000,   0.00000,   7.16157,  8.72191,
      -7.70101, 2.00363,   0.18561,   -0.28694,  1.86052,   -0.06905, 0.11829,
      0.71335,  0.09481,   -0.11182,  0.00000,   0.00000,   0.00000,  0.00000,
      0.00000,  0.00000,   0.00000,   0.00000,   0.00000,   0.00000,  0.00000,
      0.00000,  0.00000,   0.00000,   0.00000,   0.00000,   0.00000,  0.00000,
      0.00000,  0.00000,   0.00000,   0.00000,   0.00000,   0.00000,  0.00000,
      0.00000,  0.00000,   0.00000,   0.00000,   0.00000,   0.00000,  0.00000,
      0.00000,  0.00000,   0.00000,   0.00000,
  };

  void SetUp() override {
    double box[] = {13., 0., 0., 0., 13., 0., 0., 0., 13.};
    region.reinitBox(box);
    copy_coord(posi_cpy, atype_cpy, mapping, ncell, ngcell, posi, atype, rc,
               region);
    nloc = posi.size() / 3;
    nall = posi_cpy.size() / 3;
    nnei = sec_a.back();
    ndescrpt = nnei * 1;
    nat_stt.resize(3);
    ext_stt.resize(3);
    ext_end.resize(3);
    for (int dd = 0; dd < 3; ++dd) {
      ext_stt[dd] = -ngcell[dd];
      ext_end[dd] = ncell[dd] + ngcell[dd];
    }
    build_nlist(nlist_a_cpy, nlist_r_cpy, posi_cpy, nloc, rc, rc, nat_stt,
                ncell, ext_stt, ext_end, region, ncell);
    nlist.resize(static_cast<size_t>(nloc) * nnei);
    env.resize(static_cast<size_t>(nloc) * ndescrpt);
    env_deriv.resize(static_cast<size_t>(nloc) * ndescrpt * 3);
    rij_a.resize(static_cast<size_t>(nloc) * nnei * 3);
    for (int ii = 0; ii < nloc; ++ii) {
      // format nlist and record
      format_nlist_i_cpu<double>(fmt_nlist_a, posi_cpy, atype_cpy, ii,
                                 nlist_a_cpy[ii], rc, sec_a);
      for (int jj = 0; jj < nnei; ++jj) {
        nlist[ii * nnei + jj] = fmt_nlist_a[jj];
      }
      std::vector<double> t_env, t_env_deriv, t_rij_a;
      // compute env_mat and its deriv, record
      deepmd::env_mat_r_cpu<double>(t_env, t_env_deriv, t_rij_a, posi_cpy,
                                    atype_cpy, ii, fmt_nlist_a, sec_a, rc_smth,
                                    rc);
      for (int jj = 0; jj < ndescrpt; ++jj) {
        env[ii * ndescrpt + jj] = t_env[jj];
        for (int dd = 0; dd < 3; ++dd) {
          env_deriv[ii * ndescrpt * 3 + jj * 3 + dd] = t_env_deriv[jj * 3 + dd];
        }
      }
    }
    net_deriv.resize(static_cast<size_t>(nloc) * ndescrpt);
    for (int ii = 0; ii < nloc * ndescrpt; ++ii) {
      net_deriv[ii] = 10 - ii * 0.01;
    }
    double_vec(nlist);
    double_vec(net_deriv);
    double_vec(env_deriv);
    double_vec(expected_force);
  }
  void TearDown() override {}
};

TEST_F(TestProdForceR, cpu) {
  std::vector<double> force(nframes * nall * 3);
  int n_a_sel = nnei;
  deepmd::prod_force_r_cpu<double>(&force[0], &net_deriv[0], &env_deriv[0],
                                   &nlist[0], nloc, nall, nnei, nframes);
  EXPECT_EQ(force.size(), nframes * nall * 3);
  EXPECT_EQ(force.size(), expected_force.size());
  for (int jj = 0; jj < force.size(); ++jj) {
    EXPECT_LT(fabs(force[jj] - expected_force[jj]), 1e-5);
  }
  // for (int jj = 0; jj < nall * 3; ++jj){
  //   printf("%8.5f, ", force[jj]);
  // }
  // printf("\n");
}

#if GOOGLE_CUDA || TENSORFLOW_USE_ROCM
TEST_F(TestProdForceR, gpu) {
  std::vector<double> force(nframes * nall * 3, 0.0);
  int n_a_sel = nnei;

  int* nlist_dev = NULL;
  double *force_dev = NULL, *net_deriv_dev = NULL, *env_deriv_dev = NULL;

  deepmd::malloc_device_memory_sync(nlist_dev, nlist);
  deepmd::malloc_device_memory_sync(force_dev, force);
  deepmd::malloc_device_memory_sync(net_deriv_dev, net_deriv);
  deepmd::malloc_device_memory_sync(env_deriv_dev, env_deriv);

  deepmd::prod_force_r_gpu<double>(force_dev, net_deriv_dev, env_deriv_dev,
                                   nlist_dev, nloc, nall, nnei, nframes);

  deepmd::memcpy_device_to_host(force_dev, force);
  deepmd::delete_device_memory(nlist_dev);
  deepmd::delete_device_memory(force_dev);
  deepmd::delete_device_memory(net_deriv_dev);
  deepmd::delete_device_memory(env_deriv_dev);

  EXPECT_EQ(force.size(), nframes * nall * 3);
  EXPECT_EQ(force.size(), expected_force.size());
  for (int jj = 0; jj < force.size(); ++jj) {
    EXPECT_LT(fabs(force[jj] - expected_force[jj]), 1e-5);
  }
}
#endif  // GOOGLE_CUDA || TENSORFLOW_USE_ROCM
